Overview

Dataset statistics

Number of variables18
Number of observations6966
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory836.9 KiB
Average record size in memory123.0 B

Variable types

Categorical6
Numeric9
Boolean3

Alerts

date has a high cardinality: 364 distinct values High cardinality
month is highly correlated with yearHigh correlation
year is highly correlated with monthHigh correlation
dayofweek_n is highly correlated with working_dayHigh correlation
working_day is highly correlated with dayofweek_nHigh correlation
month is highly correlated with yearHigh correlation
year is highly correlated with monthHigh correlation
dayofweek_n is highly correlated with working_dayHigh correlation
working_day is highly correlated with dayofweek_nHigh correlation
hour is highly correlated with rain and 4 other fieldsHigh correlation
rain is highly correlated with hour and 4 other fieldsHigh correlation
temp is highly correlated with year and 3 other fieldsHigh correlation
rhum is highly correlated with year and 3 other fieldsHigh correlation
wdsp is highly correlated with year and 3 other fieldsHigh correlation
day is highly correlated with year and 3 other fieldsHigh correlation
month is highly correlated with year and 3 other fieldsHigh correlation
year is highly correlated with hour and 9 other fieldsHigh correlation
holiday is highly correlated with hour and 9 other fieldsHigh correlation
dayofweek_n is highly correlated with working_day and 1 other fieldsHigh correlation
working_day is highly correlated with hour and 9 other fieldsHigh correlation
peak is highly correlated with hour and 9 other fieldsHigh correlation
dayofweek is highly correlated with working_dayHigh correlation
year is highly correlated with seasonHigh correlation
season is highly correlated with yearHigh correlation
working_day is highly correlated with dayofweekHigh correlation
hour is highly correlated with peak and 1 other fieldsHigh correlation
rain is highly correlated with rain_typeHigh correlation
temp is highly correlated with month and 2 other fieldsHigh correlation
month is highly correlated with temp and 2 other fieldsHigh correlation
year is highly correlated with temp and 2 other fieldsHigh correlation
dayofweek_n is highly correlated with dayofweek and 1 other fieldsHigh correlation
dayofweek is highly correlated with dayofweek_n and 1 other fieldsHigh correlation
working_day is highly correlated with dayofweek_n and 2 other fieldsHigh correlation
season is highly correlated with temp and 2 other fieldsHigh correlation
peak is highly correlated with hour and 1 other fieldsHigh correlation
timesofday is highly correlated with hourHigh correlation
rain_type is highly correlated with rainHigh correlation
date is uniformly distributed Uniform
hour has 266 (3.8%) zeros Zeros
rain has 6325 (90.8%) zeros Zeros
dayofweek_n has 962 (13.8%) zeros Zeros

Reproduction

Analysis started2022-04-20 20:06:16.164377
Analysis finished2022-04-20 20:06:28.778727
Duration12.61 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

date
Categorical

HIGH CARDINALITY
UNIFORM

Distinct364
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size54.5 KiB
2021-12-15
 
24
2021-07-24
 
24
2022-02-05
 
24
2022-02-04
 
24
2021-06-27
 
23
Other values (359)
6847 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-03-01
2nd row2021-03-01
3rd row2021-03-01
4th row2021-03-01
5th row2021-03-01

Common Values

ValueCountFrequency (%)
2021-12-1524
 
0.3%
2021-07-2424
 
0.3%
2022-02-0524
 
0.3%
2022-02-0424
 
0.3%
2021-06-2723
 
0.3%
2021-09-0423
 
0.3%
2021-08-0123
 
0.3%
2022-02-1623
 
0.3%
2022-02-1123
 
0.3%
2021-09-1123
 
0.3%
Other values (354)6732
96.6%

Length

2022-04-20T21:06:28.845128image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-12-1524
 
0.3%
2022-02-0424
 
0.3%
2021-07-2424
 
0.3%
2022-02-0524
 
0.3%
2021-09-0423
 
0.3%
2021-08-0123
 
0.3%
2022-02-1623
 
0.3%
2022-02-1123
 
0.3%
2021-09-1123
 
0.3%
2022-01-2723
 
0.3%
Other values (354)6732
96.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

hour
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.82931381
Minimum0
Maximum23
Zeros266
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size54.5 KiB
2022-04-20T21:06:28.938653image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median13
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.3472844
Coefficient of variation (CV)0.4947485496
Kurtosis-0.8357483945
Mean12.82931381
Median Absolute Deviation (MAD)5
Skewness-0.2775842676
Sum89369
Variance40.28801926
MonotonicityNot monotonic
2022-04-20T21:06:29.049276image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
17360
 
5.2%
18358
 
5.1%
14358
 
5.1%
13356
 
5.1%
11355
 
5.1%
15354
 
5.1%
16352
 
5.1%
12352
 
5.1%
9352
 
5.1%
10349
 
5.0%
Other values (14)3420
49.1%
ValueCountFrequency (%)
0266
3.8%
1168
2.4%
2149
2.1%
3130
 
1.9%
4117
 
1.7%
5134
 
1.9%
6222
3.2%
7293
4.2%
8348
5.0%
9352
5.1%
ValueCountFrequency (%)
23278
4.0%
22306
4.4%
21317
4.6%
20344
4.9%
19348
5.0%
18358
5.1%
17360
5.2%
16352
5.1%
15354
5.1%
14358
5.1%

rain
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct44
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05960378984
Minimum0
Maximum10.3
Zeros6325
Zeros (%)90.8%
Negative0
Negative (%)0.0%
Memory size54.5 KiB
2022-04-20T21:06:29.177549image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.3
Maximum10.3
Range10.3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3295045132
Coefficient of variation (CV)5.528247685
Kurtosis209.3943547
Mean0.05960378984
Median Absolute Deviation (MAD)0
Skewness11.40684525
Sum415.2
Variance0.1085732242
MonotonicityNot monotonic
2022-04-20T21:06:29.316240image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
06325
90.8%
0.1194
 
2.8%
0.286
 
1.2%
0.356
 
0.8%
0.444
 
0.6%
0.638
 
0.5%
0.528
 
0.4%
0.726
 
0.4%
0.821
 
0.3%
0.919
 
0.3%
Other values (34)129
 
1.9%
ValueCountFrequency (%)
06325
90.8%
0.1194
 
2.8%
0.286
 
1.2%
0.356
 
0.8%
0.444
 
0.6%
0.528
 
0.4%
0.638
 
0.5%
0.726
 
0.4%
0.821
 
0.3%
0.919
 
0.3%
ValueCountFrequency (%)
10.31
< 0.1%
5.51
< 0.1%
5.21
< 0.1%
5.11
< 0.1%
4.91
< 0.1%
4.71
< 0.1%
4.61
< 0.1%
4.51
< 0.1%
4.21
< 0.1%
3.61
< 0.1%

temp
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct284
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.74239162
Minimum-4
Maximum26.3
Zeros7
Zeros (%)0.1%
Negative54
Negative (%)0.8%
Memory size54.5 KiB
2022-04-20T21:06:29.444935image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile2.6
Q17.025
median10.6
Q314.5
95-th percentile18.775
Maximum26.3
Range30.3
Interquartile range (IQR)7.475

Descriptive statistics

Standard deviation5.002159358
Coefficient of variation (CV)0.4656467141
Kurtosis-0.4056822749
Mean10.74239162
Median Absolute Deviation (MAD)3.7
Skewness0.09826056311
Sum74831.5
Variance25.02159824
MonotonicityNot monotonic
2022-04-20T21:06:29.586005image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.169
 
1.0%
866
 
0.9%
10.764
 
0.9%
10.664
 
0.9%
13.264
 
0.9%
7.663
 
0.9%
8.963
 
0.9%
8.762
 
0.9%
9.362
 
0.9%
11.360
 
0.9%
Other values (274)6329
90.9%
ValueCountFrequency (%)
-41
 
< 0.1%
-3.41
 
< 0.1%
-3.31
 
< 0.1%
-2.93
< 0.1%
-2.81
 
< 0.1%
-2.61
 
< 0.1%
-2.51
 
< 0.1%
-2.31
 
< 0.1%
-2.11
 
< 0.1%
-22
< 0.1%
ValueCountFrequency (%)
26.33
< 0.1%
26.21
 
< 0.1%
25.91
 
< 0.1%
25.72
< 0.1%
25.61
 
< 0.1%
25.43
< 0.1%
25.32
< 0.1%
25.21
 
< 0.1%
25.12
< 0.1%
251
 
< 0.1%

rhum
Real number (ℝ≥0)

HIGH CORRELATION

Distinct69
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.54593741
Minimum24
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.5 KiB
2022-04-20T21:06:29.729803image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile58
Q173
median82
Q390
95-th percentile97
Maximum100
Range76
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.91872934
Coefficient of variation (CV)0.1479743079
Kurtosis0.2126581319
Mean80.54593741
Median Absolute Deviation (MAD)8
Skewness-0.7111188468
Sum561083
Variance142.0561091
MonotonicityNot monotonic
2022-04-20T21:06:29.870413image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87256
 
3.7%
88255
 
3.7%
82251
 
3.6%
84238
 
3.4%
89232
 
3.3%
79230
 
3.3%
85223
 
3.2%
86222
 
3.2%
83222
 
3.2%
91222
 
3.2%
Other values (59)4615
66.3%
ValueCountFrequency (%)
241
 
< 0.1%
311
 
< 0.1%
321
 
< 0.1%
331
 
< 0.1%
361
 
< 0.1%
371
 
< 0.1%
381
 
< 0.1%
392
< 0.1%
404
0.1%
413
< 0.1%
ValueCountFrequency (%)
100100
1.4%
9963
 
0.9%
9888
 
1.3%
97136
2.0%
96148
2.1%
95200
2.9%
94188
2.7%
93208
3.0%
92200
2.9%
91222
3.2%

wdsp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct33
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.811369509
Minimum1
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.5 KiB
2022-04-20T21:06:30.116746image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q16
median8
Q311
95-th percentile17
Maximum35
Range34
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.38365003
Coefficient of variation (CV)0.4974992849
Kurtosis1.645777081
Mean8.811369509
Median Absolute Deviation (MAD)3
Skewness1.002281537
Sum61380
Variance19.21638759
MonotonicityNot monotonic
2022-04-20T21:06:30.243560image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
7722
10.4%
6702
10.1%
8660
9.5%
5618
8.9%
9573
 
8.2%
10560
 
8.0%
4508
 
7.3%
11464
 
6.7%
12346
 
5.0%
3340
 
4.9%
Other values (23)1473
21.1%
ValueCountFrequency (%)
131
 
0.4%
2163
 
2.3%
3340
4.9%
4508
7.3%
5618
8.9%
6702
10.1%
7722
10.4%
8660
9.5%
9573
8.2%
10560
8.0%
ValueCountFrequency (%)
352
 
< 0.1%
331
 
< 0.1%
311
 
< 0.1%
305
0.1%
294
0.1%
284
0.1%
274
0.1%
263
 
< 0.1%
256
0.1%
249
0.1%

day
Real number (ℝ≥0)

HIGH CORRELATION

Distinct31
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.64053976
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.5 KiB
2022-04-20T21:06:30.370497image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.689671829
Coefficient of variation (CV)0.5555864414
Kurtosis-1.177659354
Mean15.64053976
Median Absolute Deviation (MAD)7
Skewness0.004253637489
Sum108952
Variance75.51039649
MonotonicityNot monotonic
2022-04-20T21:06:30.487496image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
24240
 
3.4%
18239
 
3.4%
11236
 
3.4%
22236
 
3.4%
12236
 
3.4%
13235
 
3.4%
23235
 
3.4%
15235
 
3.4%
17234
 
3.4%
16234
 
3.4%
Other values (21)4606
66.1%
ValueCountFrequency (%)
1224
3.2%
2225
3.2%
3225
3.2%
4232
3.3%
5231
3.3%
6225
3.2%
7234
3.4%
8225
3.2%
9232
3.3%
10222
3.2%
ValueCountFrequency (%)
31106
1.5%
30192
2.8%
29210
3.0%
28209
3.0%
27233
3.3%
26230
3.3%
25227
3.3%
24240
3.4%
23235
3.4%
22236
3.4%

month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.557565317
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.5 KiB
2022-04-20T21:06:30.596254image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.437607482
Coefficient of variation (CV)0.5242200901
Kurtosis-1.196971805
Mean6.557565317
Median Absolute Deviation (MAD)3
Skewness-0.04555345272
Sum45680
Variance11.8171452
MonotonicityNot monotonic
2022-04-20T21:06:30.685260image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8618
8.9%
10617
8.9%
7600
8.6%
6599
8.6%
1599
8.6%
9585
8.4%
11583
8.4%
5566
8.1%
12564
8.1%
3555
8.0%
Other values (2)1080
15.5%
ValueCountFrequency (%)
1599
8.6%
2544
7.8%
3555
8.0%
4536
7.7%
5566
8.1%
6599
8.6%
7600
8.6%
8618
8.9%
9585
8.4%
10617
8.9%
ValueCountFrequency (%)
12564
8.1%
11583
8.4%
10617
8.9%
9585
8.4%
8618
8.9%
7600
8.6%
6599
8.6%
5566
8.1%
4536
7.7%
3555
8.0%

year
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size54.5 KiB
2021
5823 
2022
1143 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
20215823
83.6%
20221143
 
16.4%

Length

2022-04-20T21:06:30.775745image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-20T21:06:30.833274image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
20215823
83.6%
20221143
 
16.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

holiday
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
False
6833 
True
 
133
ValueCountFrequency (%)
False6833
98.1%
True133
 
1.9%
2022-04-20T21:06:30.872643image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

dayofweek_n
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.031007752
Minimum0
Maximum6
Zeros962
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size54.5 KiB
2022-04-20T21:06:30.933166image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.989971351
Coefficient of variation (CV)0.6565378625
Kurtosis-1.241328444
Mean3.031007752
Median Absolute Deviation (MAD)2
Skewness-0.02522150698
Sum21114
Variance3.959985976
MonotonicityNot monotonic
2022-04-20T21:06:31.017442image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
51031
14.8%
41021
14.7%
2997
14.3%
3993
14.3%
6988
14.2%
1974
14.0%
0962
13.8%
ValueCountFrequency (%)
0962
13.8%
1974
14.0%
2997
14.3%
3993
14.3%
41021
14.7%
51031
14.8%
6988
14.2%
ValueCountFrequency (%)
6988
14.2%
51031
14.8%
41021
14.7%
3993
14.3%
2997
14.3%
1974
14.0%
0962
13.8%

dayofweek
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size54.5 KiB
Saturday
1031 
Friday
1021 
Wednesday
997 
Thursday
993 
Sunday
988 
Other values (2)
1936 

Length

Max length9
Median length7
Mean length7.150301464
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMonday
2nd rowMonday
3rd rowMonday
4th rowMonday
5th rowMonday

Common Values

ValueCountFrequency (%)
Saturday1031
14.8%
Friday1021
14.7%
Wednesday997
14.3%
Thursday993
14.3%
Sunday988
14.2%
Tuesday974
14.0%
Monday962
13.8%

Length

2022-04-20T21:06:31.117040image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-20T21:06:31.189021image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
saturday1031
14.8%
friday1021
14.7%
wednesday997
14.3%
thursday993
14.3%
sunday988
14.2%
tuesday974
14.0%
monday962
13.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

working_day
Boolean

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
True
4833 
False
2133 
ValueCountFrequency (%)
True4833
69.4%
False2133
30.6%
2022-04-20T21:06:31.254524image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

season
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size54.5 KiB
Summer
1847 
Autumn
1741 
Spring
1704 
Winter
1674 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWinter
2nd rowWinter
3rd rowWinter
4th rowWinter
5th rowWinter

Common Values

ValueCountFrequency (%)
Summer1847
26.5%
Autumn1741
25.0%
Spring1704
24.5%
Winter1674
24.0%

Length

2022-04-20T21:06:31.326947image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-20T21:06:31.398926image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
summer1847
26.5%
autumn1741
25.0%
spring1704
24.5%
winter1674
24.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

peak
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
False
4585 
True
2381 
ValueCountFrequency (%)
False4585
65.8%
True2381
34.2%
2022-04-20T21:06:31.447011image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

timesofday
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size54.5 KiB
Afternoon
2132 
Morning
1697 
Evening
1673 
Night
1464 

Length

Max length9
Median length7
Mean length7.191788688
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNight
2nd rowMorning
3rd rowMorning
4th rowMorning
5th rowMorning

Common Values

ValueCountFrequency (%)
Afternoon2132
30.6%
Morning1697
24.4%
Evening1673
24.0%
Night1464
21.0%

Length

2022-04-20T21:06:31.521479image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-20T21:06:31.594701image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
afternoon2132
30.6%
morning1697
24.4%
evening1673
24.0%
night1464
21.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

rain_type
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size54.5 KiB
no rain
6325 
drizzle
 
336
moderate rain
 
224
light rain
 
72
heavy rain
 
9

Length

Max length13
Median length7
Mean length7.227820844
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno rain
2nd rowno rain
3rd rowno rain
4th rowno rain
5th rowno rain

Common Values

ValueCountFrequency (%)
no rain6325
90.8%
drizzle336
 
4.8%
moderate rain224
 
3.2%
light rain72
 
1.0%
heavy rain9
 
0.1%

Length

2022-04-20T21:06:31.682046image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-20T21:06:31.751915image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
rain6630
48.8%
no6325
46.5%
drizzle336
 
2.5%
moderate224
 
1.6%
light72
 
0.5%
heavy9
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

count
Real number (ℝ≥0)

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.754378409
Minimum1
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.5 KiB
2022-04-20T21:06:31.837118image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile11
Maximum26
Range25
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.442080321
Coefficient of variation (CV)0.7239811442
Kurtosis1.566956878
Mean4.754378409
Median Absolute Deviation (MAD)2
Skewness1.177331745
Sum33119
Variance11.84791694
MonotonicityNot monotonic
2022-04-20T21:06:31.941307image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
11229
17.6%
21000
14.4%
3898
12.9%
4775
11.1%
5645
9.3%
6597
8.6%
7471
 
6.8%
8368
 
5.3%
9274
 
3.9%
10208
 
3.0%
Other values (14)501
7.2%
ValueCountFrequency (%)
11229
17.6%
21000
14.4%
3898
12.9%
4775
11.1%
5645
9.3%
6597
8.6%
7471
 
6.8%
8368
 
5.3%
9274
 
3.9%
10208
 
3.0%
ValueCountFrequency (%)
261
 
< 0.1%
242
 
< 0.1%
231
 
< 0.1%
211
 
< 0.1%
205
 
0.1%
198
 
0.1%
188
 
0.1%
1716
0.2%
1619
0.3%
1532
0.5%

Interactions

2022-04-20T21:06:27.302720image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:19.167633image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:20.139693image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:21.069183image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:22.035039image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:23.066062image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:24.072082image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:25.046167image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:26.296677image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:27.400598image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:19.272350image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:20.248804image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:21.165671image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:22.140713image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:23.170704image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:24.173096image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:25.450600image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:26.444719image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:27.498994image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:19.372465image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:20.353083image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:21.277298image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:22.252236image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:23.278400image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:24.275501image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:25.563652image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:26.549051image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:27.605569image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:19.474137image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:20.455852image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:21.391298image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:22.365743image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:23.402555image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:24.385492image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:25.668853image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:26.665799image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:27.716827image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:19.587209image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:20.565237image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:21.501006image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:22.483914image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:23.525849image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:24.499335image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:25.781394image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:26.775188image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:27.828564image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:19.704022image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:20.669616image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:21.607816image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:22.608563image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:23.639639image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:24.610663image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:25.883468image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:26.879925image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:27.938959image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:19.811797image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:20.768298image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:21.711258image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:22.722251image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:23.748459image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:24.719047image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:25.982014image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:26.987547image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:28.048270image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:19.921793image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:20.866526image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:21.816839image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:22.834227image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:23.851877image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:24.825654image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:26.091923image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:27.091506image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:28.157648image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:20.034268image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:20.968377image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:21.925890image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:22.947176image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:23.963876image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:24.934968image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:26.194935image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-20T21:06:27.197385image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2022-04-20T21:06:32.043833image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-20T21:06:32.214556image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-20T21:06:32.378677image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-20T21:06:32.896850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-04-20T21:06:33.051440image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-20T21:06:28.382782image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-20T21:06:28.667161image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

datehourraintemprhumwdspdaymonthyearholidaydayofweek_ndayofweekworking_dayseasonpeaktimesofdayrain_typecount
02021-03-0120.0-1.2984132021False0MondayTrueWinterFalseNightno rain1
12021-03-0170.02.11004132021False0MondayTrueWinterTrueMorningno rain3
22021-03-0180.05.1985132021False0MondayTrueWinterTrueMorningno rain1
32021-03-0190.05.7985132021False0MondayTrueWinterTrueMorningno rain4
42021-03-01100.06.7946132021False0MondayTrueWinterTrueMorningno rain4
52021-03-01110.07.4918132021False0MondayTrueWinterFalseMorningno rain4
62021-03-01120.06.9888132021False0MondayTrueWinterFalseAfternoonno rain8
72021-03-01130.09.3848132021False0MondayTrueWinterFalseAfternoonno rain11
82021-03-01140.09.3809132021False0MondayTrueWinterFalseAfternoonno rain11
92021-03-01150.08.37911132021False0MondayTrueWinterTrueAfternoonno rain10

Last rows

datehourraintemprhumwdspdaymonthyearholidaydayofweek_ndayofweekworking_dayseasonpeaktimesofdayrain_typecount
69562022-02-27130.08.678152722022False6SundayFalseWinterFalseAfternoonno rain8
69572022-02-27140.08.980172722022False6SundayFalseWinterFalseAfternoonno rain10
69582022-02-27150.08.684162722022False6SundayFalseWinterFalseAfternoonno rain4
69592022-02-27160.08.786172722022False6SundayFalseWinterFalseAfternoonno rain3
69602022-02-27170.08.589162722022False6SundayFalseWinterFalseAfternoonno rain8
69612022-02-27180.08.770102722022False6SundayFalseWinterFalseEveningno rain4
69622022-02-27190.08.07292722022False6SundayFalseWinterFalseEveningno rain2
69632022-02-27200.08.666142722022False6SundayFalseWinterFalseEveningno rain1
69642022-02-27210.09.068112722022False6SundayFalseWinterFalseEveningno rain2
69652022-02-27220.28.674102722022False6SundayFalseWinterFalseEveningdrizzle2